Biofilter as a Functional Annotation Pipeline for Common and Rare Copy Number Burden

Kim, Dokyoon; Lucas, Anastasia; Glessner, Joseph; Verma, Shefali S.; Bradford, Yukiko; Li, Ruowang; Frase, Alex T.; Hákonarson, Hákon; Peissig, Peggy L.; Brilliant, Murray H.; Ritchie, Marylyn D.

doi:10.1142/9789814749411_0033

Cited by 2 publications

(6 citation statements)

References 25 publications

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“…To test this, we mapped only genes present in the KEGG pathways that also contain GANC : metabolic pathways, starch and sucrose metabolism, and galactose metabolism, to CNVs. We then applied a permutation test developed for CNV annotation, the details of which are outlined previously 15 and in Methods.…”

Section: Resultsmentioning

confidence: 99%

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PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

et al. 2017

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Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene–environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits.

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Section: Resultsmentioning

confidence: 99%

“…To assess whether there was deletion enrichment in genes relating to GANC , we applied a permutation test using R version 3.2.1 that was previously developed for CNV annotation, the details of which are outlined previously 15 . This method tests the hypothesis that a specific gene contains more deletions in T2D cases than in controls.…”

Section: Methodsmentioning

confidence: 99%

PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

et al. 2017

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“…4,950 of pairwise combinations of each phenotype’s top SNPs were assessed for interaction as the main effect filtering. All SNPs passing the genotype quality control were imported into Biofilter software to generate SNP-SNP interaction pairs [ 8 , 10 , 23 ]. Biofilter software builds SNP-SNP pairs by referencing publicly available biological knowledge, including protein-protein interactions, pathways, and ontological information.…”

Section: Methodsmentioning

confidence: 99%

“…The top one hundred SNPs were selected from each phenotype's GWAS based on power estimations with 1000 replications in R software using logistic regression model for cardiac traits and linear regression model for fatty acids (S3A and S3B Fig SNPs were assessed for interaction as the main effect filtering. All SNPs passing the genotype quality control were imported into Biofilter software to generate SNP-SNP interaction pairs [8,10,23]. Biofilter software builds SNP-SNP pairs by referencing publicly available biological knowledge, including protein-protein interactions, pathways, and ontological information.…”

Section: Plos Onementioning

confidence: 99%

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Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study

et al. 2020

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Epistasis analysis elucidates the effects of gene-gene interactions (G×G) between multiple loci for complex traits. However, the large computational demands and the high multiple testing burden impede their discoveries. Here, we illustrate the utilization of two methods, main effect filtering based on individual GWAS results and biological knowledge-based modeling through Biofilter software, to reduce the number of interactions tested among single nucleotide polymorphisms (SNPs) for 15 cardiac-related traits and 14 fatty acids. We performed interaction analyses using the two filtering methods, adjusting for age, sex, body mass index (BMI), waist-hip ratio, and the first three principal components from genetic data, among 2,824 samples from the Ludwigshafen Risk and Cardiovascular (LURIC) Health Study. Using Biofilter, one interaction nearly met Bonferroni significance: an interaction between rs7735781 in XRCC4 and rs10804247 in XRCC5 was identified for venous thrombosis with a Bonferroni-adjusted likelihood ratio test (LRT) p: 0.0627. A total of 57 interactions were identified from main effect filtering for the cardiac traits G×G (10) and fatty acids G×G (47) at Bonferroni-adjusted LRT p < 0.05. For cardiac traits, the top interaction involved SNPs rs1383819 in SNTG1 and rs1493939 (138kb from 5' of SAMD12) with Bonferroni-adjusted LRT p: 0.0228 which was significantly associated with history of arterial hypertension. For fatty acids, the top interaction between rs4839193 in KCND3 and rs10829717 in LOC107984002 with Bonferroni-adjusted LRT p: 2.28×10 −5 was associated with 9-trans 12-trans octadecanoic acid, an omega-6 trans fatty acid. The model inflation factor for the interactions under different filtering methods was evaluated from the standard median and the linear regression approach. Here, we applied filtering approaches to identify numerous genetic interactions related to cardiac-related outcomes as potential targets for therapy. The

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Biofilter as a Functional Annotation Pipeline for Common and Rare Copy Number Burden

Abstract: Recent studies on copy number variation (CNV) have suggested that

Cited by 2 publications

References 25 publications

PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study

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